EUVD-2025-34892

Comprehensive Technical Analysis of EUVD-2025-34892

1. Vulnerability Assessment and Severity Evaluation

The vulnerability described in EUVD-2025-34892 pertains to the Keras framework, which is susceptible to deserialization of untrusted data. This vulnerability is assigned a CVSS (Common Vulnerability Scoring System) base score of 9.8, indicating a critical severity level. The CVSS vector CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H breaks down as follows:

• Attack Vector (AV): Network (N) - The vulnerability can be exploited remotely over the network.
• Attack Complexity (AC): Low (L) - The attack requires minimal skill or resources.
• Privileges Required (PR): None (N) - No special privileges are needed to exploit the vulnerability.
• User Interaction (UI): None (N) - No user interaction is required for the attack to succeed.
• Scope (S): Unchanged (U) - The vulnerability does not change the security scope.
• Confidentiality (C): High (H) - The vulnerability can lead to a significant breach of confidentiality.
• Integrity (I): High (H) - The vulnerability can lead to a significant breach of integrity.
• Availability (A): High (H) - The vulnerability can lead to a significant breach of availability.

Given the high scores in confidentiality, integrity, and availability, this vulnerability poses a severe risk to systems using the affected versions of Keras.

2. Potential Attack Vectors and Exploitation Methods

The primary attack vector for this vulnerability is the deserialization of untrusted data. An attacker could exploit this by:

• Crafting Malicious Input: An attacker could send specially crafted input data to a Keras application that deserializes this data without proper validation.
• Remote Code Execution (RCE): If the deserialization process is not secure, it could lead to arbitrary code execution on the target system.
• Data Manipulation: An attacker could manipulate the data being deserialized to inject malicious payloads, leading to data corruption or unauthorized access.

3. Affected Systems and Software Versions

The vulnerability affects Keras versions 3.11.0 through 3.11.3. Systems and applications that use these versions of Keras for machine learning tasks are at risk. This includes:

• Machine Learning Models: Applications that load and execute machine learning models using Keras.
• Data Processing Pipelines: Systems that use Keras for data preprocessing and feature extraction.
• Research and Development Environments: Academic and industrial research environments that rely on Keras for experimentation and development.

4. Recommended Mitigation Strategies

To mitigate the risk associated with this vulnerability, the following strategies are recommended:

• Update to the Latest Version: Upgrade to Keras version 3.11.3 or later, which includes the security patch for this vulnerability.
• Input Validation: Implement robust input validation to ensure that only trusted data is deserialized.
• Use Secure Deserialization Libraries: Consider using secure deserialization libraries or frameworks that provide built-in protection against such vulnerabilities.
• Network Segmentation: Segment the network to limit the attack surface and reduce the risk of remote exploitation.
• Regular Security Audits: Conduct regular security audits and vulnerability assessments to identify and mitigate similar issues.

5. Impact on European Cybersecurity Landscape

The vulnerability in Keras has significant implications for the European cybersecurity landscape, particularly in sectors that heavily rely on machine learning and artificial intelligence, such as:

• Financial Services: Banks and financial institutions using Keras for fraud detection and risk assessment.
• Healthcare: Medical research and diagnostic systems that use Keras for data analysis.
• Industrial Control Systems: Manufacturing and industrial automation systems that employ Keras for predictive maintenance.

Given the critical nature of these sectors, the vulnerability could lead to data breaches, financial losses, and disruptions in critical infrastructure.

6. Technical Details for Security Professionals

For security professionals, the following technical details are pertinent:

• Vulnerability Identification: The vulnerability is identified by CVE-2025-49655 and EUVD-2025-34892.
• References:
  • HiddenLayer Security Advisor
  • Keras GitHub Pull Request
  • NVD Detail
• Assigner: HiddenLayer
• ENISA ID Product: Keras versions 3.11.0 through 3.11.3
• ENISA ID Vendor: Keras

Security professionals should review the provided references for detailed technical information and guidance on patching and mitigation strategies.

Conclusion

The deserialization vulnerability in Keras versions 3.11.0 through 3.11.3 poses a critical risk to systems and applications using these versions. Immediate action is required to update to the latest version and implement additional security measures to mitigate the risk. The impact on the European cybersecurity landscape underscores the importance of prompt and effective mitigation strategies.